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It is often necessary to prune the trees which causes nuisance to public and transportation on the road sides or even within the house premises. Alternatively farmers struggle to reach to heights in order to harvest fruits in their orchards. Most of the robots used in pruning and harvesting fruits are very high cost and used in developed nations. T...
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... leads to overheating of the motor and its wires. Table 1 shows the materials used to build the robotic arm. Aluminium rods make up the three sections of the robotic armone meant to attach the arm to the host machine, while the other two provide for the dual degrees of freedom. ...Context 2
... is lightweight, as compared to other metals, which is why it is chosen. The dimensions provided in Table 1 are the outcome of numerous load tests. Geared motor, owing to its wider base area, provide extra stability to the system as such. ...Similar publications
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The PLC is widely used as a controller with input in the form of switches and sensors and output in the form of coils. PLC can be connected with HMI to facilitate the operation of the PLC. The usual HMI is installed on a PLC in the form of a stand-alone PC HMI and HMI that is connected via serial cable or Ethernet cable. The HMI in the form of wire...
Citations
... Hence, mechanization seems to be the efficient solution because it is the only way to reduce harvesting labor costs, allowing growers to remain competitive in the future and even expanding markets [12]. To deal with labor shortages and provide a financially viable solution to rapidly rising labor costs, the use of picking robots has been introduced [13][14][15][16]. ...
The agricultural sector has been rendered under tremendous strain due to rapid population growth. However, with technological advancements, this decade has been witnessing a paradigm shift from traditional to smart agriculture systems that boost precisions and productions through sensor technologies, different types of machinery, and robots. As a result, significant advances have been made in planting, weed removing, harvesting of crops, disease recognition of plants, damage and defect assessment of fruits and vegetables, their quality grading, identification of pests and insects, etc. Harvesting is a very important step in collecting crops from the fields where robots are playing a great role in bringing precision, efficiency, and effectiveness. This chapter describes the automated harvesting of some common fruits and vegetables, such as tomatoes, apples, litchi, sweet peppers, and kiwifruit with the help of robots. The limitations of the existing harvesting robots are pointed out, and suggestions are made for new research directions for further advancements.KeywordsAgriculture automationSmart agricultureHarvesting robotsSensor technology
... 3) Message Queue Telemetry Transport (MQTT): is a light and very simple publish/subscribe messaging protocol developed for unreliable networks with higher latency or low bandwidth. It is designed to ensure that network bandwidth and device resource requirements are minimized, while trying Crop-field monitoring and automation irrigation system Field monitoring [156]- [160] Unauthorised actions detection [161], [162] Remote sensing [64] Motion detection [163] Objects identification [32] Light, Gaz, PH and Temperature monitoring [85] Multimedia data acquisition [63] Smart irrigation [164]- [167] Desalination [90], [168] Soil moisture measurement [169] Weather forecast [170] Water quality and pressure monitoring [49] Humidity monitoring [171], [172] Decision support systems [105] Water loss control [173] Rain detection [174] Fertilization [175] Pest control [176] Herbicides application [177] Solar pest control light [178] UAV-based agrochemicals spraying [179] Weed detection [180] Insecticides application [181] Soil NPK sensing [182] Crop health monitoring [183]- [185] Livestock health monitoring [186], [187] Disease prediction [188] Behaviors monitoring [189] Disease detection [190] Disease prevention [191] Blood pressure and heart rate monitoring [192] Disease classification [193] Objects detection [194], [195] Robotics arms [69], [196] Motion control [197] Fruit detection and classification [198], [199] Colors and shapes recognition [200] Obstacles detection [201] Optimal harvest date [202] Products identification [33], [203] Traceability with blockchain Technology [204]- [207] Food safety and quality control [208]- [210] Agricultural mobile crowd sensing [40] Chain risk control [211] Agrivoltaic systems [212], [213] Photovoltaics farms [214], [215] Greenhouse [191], [215]- [218] Hydroponic [55], [219], [220] Aeroponics [221], [222] Aquaponics [223], [224] Vertical farming [225] Plant phenotyping [226] Fig. 7: Classification of IoT applications for smart agriculture to guarantee the reliability, and a degree of assurance in the delivery. These principles make the protocol perfect for Machine to Machine (M2M) or IoT connected devices, as well as for mobile applications, that require high bandwidth and battery power [28]. ...
... 3) Motion control: Harvesting robots can, at any time, receive direct commands from the farmer for controlling their movements, which makes the harvesting task more efficient. Megalingam et al. [197] presented the design of an inexpensive robotic arm capable of pruning and harvesting tree fruits. A mobile application developed to control the motion of the robotic arm via Bluetooth. ...
This paper presents a comprehensive review of emerging technologies for the Internet of Things-based smart agriculture. We begin by summarizing the existing surveys and describing emergent technologies for the agricultural Internet of Things (IoT), such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies, cloud/fog computing, and middleware platforms. We also provide a classification of IoT applications for smart agriculture into seven categories, including, smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs. Furthermore, we present real projects that use most of the technologies mentioned above, achieving great performance in the field of smart agriculture. Finally, we highlight open research challenges and discuss possible future research directions for agricultural IoTs.
... One more similar case where torque control plays a vital role is discussed in the paper [14]. Paper [15] describes the case where a robotic with a cutter as an end effector is used to cut the fruits. Here again, torque plays an important in cutting the fruits. ...
... In this setup we have used NEX Robotics Hercules motor driver to run our high torque motor. It is a 6V-36V, 15 Amp motor driver and support up to 30 Amps load. This motor driver has a maximum PWM frequency and can be interfaced with 3.3V or 5V as logic levels. ...
... Even though no machine has been developed for robotic pruning for tree fruit crops, some progress has been made in the development of robotics technology for pruning more complex canopies such as apple and cherry trees. As discussed above, most studies on robotic fruit tree pruning focus on developing machine vision system for the branch identification and reconstruction, while few studies focus on the robotic arm for simulation of pruning task [113,114]. Furthermore, as plenty of robotic systems have been developed for pruning grape vines as well as picking fruits, it would be possible to develop an effective robotic pruner for tree fruit crops when the tree architecture is getting more uniform. The challenges and solutions are discussed in the following section. ...
Pruning is one of the most important tree fruit production activities, which is highly dependent on human labor. Skilled labor is in short supply, and the increasing cost of labor is becoming a big issue for the tree fruit industry. Meanwhile, worker safety is another issue in the manual pruning. Growers are motivated to seek mechanical or robotic solutions for reducing the amount of hand labor required for pruning. Identifying tree branches/canopies with sensors as well as automated operating pruning activity are the important components in the automated pruning system. This paper reviews the research and development of sensing and automated systems for branch pruning in apple production. Tree training systems, pruning strategies, 3D structure reconstruction of tree branches, and practice mechanisms or robotics are some of the developments that need to be addressed for an effective tree branch pruning system. Our study summarizes the potential opportunities for automatic pruning with machine-friendly modern tree architectures, previous studies on sensor development, and efforts to develop and deploy mechanical/robotic systems for automated branch pruning. We also describe two examples of qualified pruning strategies that could potentially simplify the automated pruning decision and pruning end-effector design. Finally, the limitations of current pruning technologies and other challenges for automated branch pruning are described, and possible solutions are discussed.
... Even though no machine has been developed for robotic pruning for tree fruit crops, some progress has been made in the development of robotics technology for pruning more complex canopies such as apple and cherry trees. As discussed above, most studies on robotic fruit tree pruning focus on developing machine vision system for the branch identification and reconstruction, while few studies focus on the robotic arm for simulation of pruning task [113,114]. Furthermore, as plenty of robotic systems have been developed for pruning grape vines as well as picking fruits, it would be possible to develop an effective robotic pruner for tree fruit crops when the tree architecture is getting more uniform. The challenges and solutions are discussed in the following section. ...
Pruning is one of the most important tree fruit production activities, which is highly dependent on human labor. Skilled labor is in short supply, and the increasing cost of labor is becoming a big issue for the tree fruit industry. Growers are motivated to seek mechanical or robotic solutions for reducing the amount of hand labor required for pruning. This paper reviews the research and development of sensing and automated systems for branch pruning for tree fruit production. Horticultural advancements, pruning strategies, 3D structure reconstruction of tree branches, as well as practice mechanisms or robotics are some of the developments that need to be addressed for an effective tree branch pruning system. Our study summarizes the potential opportunities for automatic pruning with machine-friendly modern tree architectures, previous studies on sensor development, and efforts to develop and deploy mechanical/robotic systems for automated branch pruning. We also describe two examples of qualified pruning strategies that could potentially simplify the automated pruning decision and pruning end-effector design. Finally, the limitations of current pruning technologies and other challenges for automated branch pruning are described, and possible solutions are discussed.
... It discusses about fluid dynamics and electronic design to control droplet dispensing in such robots which are used for weed control. Our earlier paper discusses about the low cost robotic arm for pruning and fruit harvesting applications [8]. Figure 1 shows the system architecture of the proposed glove, harvester and interface. ...
One of the major challenges in agriculture is harvesting. It is very hard and sometimes even unsafe for workers to go to each plant and pluck fruits. Robotic systems are increasingly combined with new technologies to automate or semi automate labour intensive work, such as e.g. grape harvesting. In this work we propose a semi-automatic method for aid in harvesting fruits and hence increase productivity per man hour. A robotic arm fixed to a rover roams in the in orchard and the user can control it remotely using the hand glove fixed with various sensors. These sensors can position the robotic arm remotely to harvest the fruits. In this paper we discuss the design of hand glove fixed with various sensors, design of 4 DoF robotic arm and the wireless control interface. In addition the setup of the system and the testing and evaluation under lab conditions are also presented in this paper.
Position and depth (PD) estimation is one of the key characteristics of autonomous robots. Robots are often challenged to visualize an alien environment remotely, alongside the control mechanism, and estimate the PD of objects. For the robot to reach out to an object, it needs to know the object's position in a 3-D space. The design of the robot's vision system is crucial. In this research work, we propose a human intervention-based hybrid approach for estimation of PD of an object. Human intervention in the form of a mouse click on the laser spot of the object image/in the video created by a camera-laser setup is used to estimate the PD of an object. An error correction model is developed and evaluated for better performance of the proposed method. A comparison of the proposed method with that of the image processing method revealed that a hybrid approach is almost 50% better in accuracy. The test results indicate that this method could be very helpful in finding the depth of the object with better accuracy in a teleoperated semiautonomous robot.
Proposed two-link flexible manipulators suit the requirement of larger work volume than the traditional flexible manipulators and handle high payloads equal to that of rigid manipulators. The simulation was performed on the Computer-Aided Design (CAD) model of the Motoman HC-10DT, a Human-Collaborative robot. During the analysis, one link of the manipulator was modified as a flexible manipulator for which static and modal analysis was done and compared the results with the actual link of the robot to validate the proposed design. The kinematic analysis was also done to find the reach of the modified robot. Total deformation on both the flexible manipulator and the actual link of the robot was 0.2mm but the maximum von Mises stress acting on the flexible manipulator was 2.97% lesser than that of the rigid link of the robot. Eventually, the safety factor of the flexible manipulator was higher compared to the rigid link. The factor of safety of the upper link is 1.41 and the lower link is 1.51 whereas the actual link of the robot has the safety factor of 1.30.
